Basic Geographic
Concepts
GEOG 370
Instructor: Christine Erlien
Basic Geographic Concepts
Real World  Digital Environment
How are real world objects recorded in
digital format?
Directly (by instruments on the ground)
- Remotely (by satellites hundreds of miles
above the earth’s surface)
- Collected by census takers
- Extracted from documents or maps
-
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From Real World Objects to
Cartographic Objects

Real world objects differ in:
– Size
– Shape
– Color
– Pattern

These differences affect how these
objects are represented digitally
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Real World Cartographic Objects:
Description

Attributes
– Information about object (e.g., characteristics)

Location/Spatial information
– Coordinates
– May contain elevation information

Time
– When collected/created
– Why? Objects may have different attributes
over time
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Generalizing Real World Objects
Point: Location only
 Line

– 1-D: length
– Made up of a connected sequence of points

Polygon
– 2-D: length & width
– Enclosed area

Surface
– 3-D: length, width, height
– Incorporates elevation data
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Scale affects how an object is generalized
Close-up (large scale)  houses appear to have length & width
Small-scale  houses appear as points
Generalizing Spatial Objects (Cont.)

Representing an object as point? line?
polygon?
– Depends on
• Scale (small or large area)
• Data
• Purpose of your research
– Example: House
• Point (small scale mapping)
• Polygon
• 3D object (modeling a city block)
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Data: Continuous vs. discrete

Continuous
– Data values distributed across a surface w/out
interruption
– Examples: elevation, temperature

Discrete
– Occurs at a given point in space; at a given spot,
the feature is present or not
– Examples
• Points: Town, power pole
• Lines: Highway, stream
• Areas: U.S. Counties, national parks
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http://weather.unisys.com/surface/sst.gif
www.regional.org.au/au/asa/2003/i/6/walcott.htm
Continuous & discrete?

Some data types may be presented as
either discrete or continuous
– Example
• Population at a point (discrete)
• Population density surface for an area
(continuous)
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Selection of world’s largest cities
http://www.citypopulation.de/World.html
Generalities

Continuous data
– Raster

Discrete data
– Vector
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Spatial Measurement Levels
Three levels of spatial measurement:
 Nominal scale

Ordinal level

Interval/ratio
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Spatial Measurement Levels:
Nominal

Simplest/lowest level of measurement

Identification/labeling of data

Does not allow direct comparisons
between one named object and another
– Notes difference
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ESRI Mapbook 18
Spatial Measurement Levels: Ordinal
Data ranked based on a particular
characteristic
 Gives us insights into logical comparisons
of spatial objects
 Examples:

– Large, small, medium sized cities
– Interstate highway, US highway, State
highway, Country road
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ESRI Mapbook 18
Spatial Measurement Levels:
Interval
Numbers assigned to items measured
 Measured on a relative scale rather than
absolute scale

– 0 point in scale is arbitrary
Data can be compared with more precise
estimates of the differences than nominal
or ordinal levels
 Not very common

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Spatial Measurement Levels:
Interval
Example: Temperature
 Zero temperature varies according to
the unit of measurement (0 deg. C = 32
deg. F)
 0 deg. C is not the absence of heat 
Absolute zero is identified by 0 Kelvin

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Spatial Measurement Levels:
Interval

The difference between values makes sense,
but ratios of interval data do not
 Ex.: A piece of metal at 300 degrees
Fahrenheit is not twice as hot as a piece of
metal at 150 degrees Fahrenheit
– Why? the ratio of these values is different
using Celsius
150 deg. F=66 C
300 deg. F.=149 deg. C
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http://weather.unisys.com/surface/sst.gif
Spatial Measurement Levels:
Ratio
Numbers assigned to items measured
 Measured on an absolute scale (use true 0
point in scaling)

– Measurements of length, volume, density,
etc.

Data can be compared with more precise
estimates of the differences than nominal
or ordinal levels
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Spatial Measurement Levels:
Ratio

Examples
– Locational coordinates in a standard
system
– Total precipitation
– Population density
– Volume of stream discharge
– Areas of countries
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ESRI Mapbook 18
Measurement Levels &
Mathematical Comparisons

Nominal scale
– Not possible

Ordinal scale
– Compare in terms of greater than, less than,
equal to

Interval/ratio scales
– Mathematical operations
• Interval: addition, subtraction
• Ratio: add, subtract, multiply, divide
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Summarizing
We’ve been talking about
 Characterizing objects
– How to generalize/represent real world
objects?
– Attributes
– Continuous vs. discrete data types
– Spatial measurement levels
We’re moving on to location
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Spatial Location and Reference
Communicating the location of objects
 Absolute location
–
–

Definitive, measurable, fixed point in space
Requires a reference system (e.g., grid
system such as Latitude/Longitude)
Relative location
–
Location determined relative to other objects
in geographic space
•
•
Giving directions
UTM
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Spatial Location and Reference:
Latitude / Longitude

Most commonly-used coordinate system
 Lines of latitude are called parallels
 Lines of longitude are called meridians
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Latitude / Longitude

Prime Meridian & Equator are the reference points
used to define latitude and longitude
Spatial Comparisons
Pattern analysis: An important way to
understand spatial relationships
between objects.
 Three point distribution patterns:

– Regular: Uniform
– Clustered
– Random: No apparent organization
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http://en.wikipedia.org/wiki/Image:Snow-cholera-map.jpg
Describing Spatial Patterns
Proximity: Nearness
 Orientation: Azimuthal direction
(N,S,E,W) relating the spatial
arrangement of objects
 Diffusion: Objects move from one area
to another through time
 Density

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Relationships between sets of
features

Association: Spatial relationship
between different characteristics of the
same location
– Example: Vegetation-elevation

Correlation: Statistically significant
relationship between objects that are
associated spatially
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Collecting Geographic Data

Small areas
– Ground survey
– Census

Large areas
– Census (less oftenevery 10 years)
– Remote sensing
– GPS (e.g., collared animals)
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Collecting Geographic Data:
Sampling & Sampling Schemes
Sampling: When a census isn’t practical
 Types of sampling

– Directed: Based on experience, accessibility,
selection of particular study areas
– Probability-based: For the total population of
interest, each element has a known probability
of being selected
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Sampling & Sampling Schemes

Probabilistic sampling methods
– Random: Each feature has same probability
of selection
– Systematic: Repeated pattern guides sample
selection
– Homogeneous
– Stratified: Area divided based on particular
characteristics, then features sampled w/in
selected areas
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Probabilistic sampling methods
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Samples: Making inferences

Why? Sampling leaves gaps in knowledge
– What to do? Use models to predict missing
values

Interpolation: Predicting unknown values
using known values occurring at locations
around the unknown value
 Extrapolation: Predicting missing values
using existing values that exist only on one
side of the point in question
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Important Concepts from Ch.2
How real world objects may be
generalized in the digital environment
 How the representation of real world
objects may change based on the scale
of observation
 Discrete vs. continuous data
 Measurement levels: nominal, ordinal,
interval, ratio

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Important Concepts from Ch.2
Lat/long
 Absolute vs. relative location
 Describing spatial patterns
 Collecting geographic data and how it
might differ based on size of study area
 Sampling & sampling methods

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